Combining differential expression and differential coexpression analysis identifies optimal gene and gene set in cervical cancer.

OBJECTIVE:The objective of this study is to investigate the optimal gene and functional-related gene set in cervical cancer through combing the differential expression (DE) and differential coexpression (DC) analysis. MATERIALS AND METHODS:To achieve this, we first measured expression data of cervical cancer by incorporating DE and DC effects utilizing absolute t-value in t-statistic and Z-test, respectively. Then, we selected the optimal threshold pair to determine both high DE and high DC (HDE_HDC) partition on the basis of Chi-square maximization, and the best threshold pair divided all genes into four parts, including HDE_HDC, high DE and low DC (HDE_LDC), low DE and high DC (LDE_HDC), and low DE and low DC (LDE_LDC). Using the known functional gene sets, functional relevance of partition genes was explored to determine the best-associated gene set based on the functional information (FI) conception. RESULTS:Under the optimal threshold pair of 3.629 and 1.108 for DE and DC, respectively genes were divided into four partitions: HDE_HDC (311 genes), HDE_LDC (2072 genes), LDE_HDC (seventy genes), and LDE_LDC (1623 genes). Meanwhile, the gene set epidermis development was the best-associated gene set with the largest △G* = 10.496. Among the genes of epidermis development, zinc finger protein 135 (ZNF135) attained highest minimum FI gain of 41.226. CONCLUSION:The combination of DE and DC analysis showed higher mean FI relative to individual DE and DC analyses. We successfully exhibited the optimal gene set epidermis development and gene ZNF135, which might be crucial for the prevention and treatment of cervical cancer.